In recent years, web services have increasingly relied on social data in providing information to their users, where social data generally refers to content created by users (e.g., a user review of a product), which they knowingly and voluntarily share with other users. For example, on Facebook users discover content based on what their friends and other users like, and on Amazon users evaluate potential purchases based on other users' reviews. Unfortunately, attackers attempt to skew content perception by offering misleading feedback (through a variety of means), with the goal of increased distribution for their content. The challenge becomes distinguishing such fraudulent feedback from legitimate user feedback. Such a challenge is faced by all services that depend on user behavior for their processes and recommendations, for e.g., from stories on Facebook to product reviews on Amazon to reviews of businesses on TripAdvisor.
For example, on Facebook, Pages are used by organizations to interact with their fans. Users can “Like” a Page to let their friends know about their interests and to receive content from that Page in their News Feed, one of the primary distribution channels on Facebook. Further, other users may interpret a high “Like” count as a Page being popular and will also see their friends' Page Likes in their News Feeds. Because of the News Feed's utility as a distribution channel, attackers frequently attempt to boost Page Like counts to get increased distribution for their content. For instance, attackers have attempted to inflate Like counts through a variety of deceitful methods, including malware, credential stealing, social engineering, and fake accounts. Such ill-gotten Likes that came from someone not truly interested in connecting with a Page could affect the trust of users on such social data.
Among teaching a variety of other things, certain aspects of the inventions herein have embodiments which may satisfy one or more of the above-described issues.